from typing import Callable
from typing import Optional
import numpy as np
from optuna._study_direction import StudyDirection
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports
from optuna.visualization._utils import _check_plot_args
if _imports.is_successful():
from optuna.visualization._plotly_imports import go
_logger = get_logger(__name__)
[文档]def plot_optimization_history(
study: Study,
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "go.Figure":
"""Plot optimization history of all trials in a study.
Example:
The following code snippet shows how to plot optimization history.
.. plotly::
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x ** 2 + y
sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)
fig = optuna.visualization.plot_optimization_history(study)
fig.show()
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their target values.
target:
A function to specify the value to display. If it is :obj:`None` and ``study`` is being
used for single-objective optimization, the objective values are plotted.
.. note::
Specify this argument if ``study`` is being used for multi-objective optimization.
target_name:
Target's name to display on the axis label and the legend.
Returns:
A :class:`plotly.graph_objs.Figure` object.
Raises:
:exc:`ValueError`:
If ``target`` is :obj:`None` and ``study`` is being used for multi-objective
optimization.
"""
_imports.check()
_check_plot_args(study, target, target_name)
return _get_optimization_history_plot(study, target, target_name)
def _get_optimization_history_plot(
study: Study,
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> "go.Figure":
layout = go.Layout(
title="Optimization History Plot",
xaxis={"title": "#Trials"},
yaxis={"title": target_name},
)
trials = [t for t in study.trials if t.state == TrialState.COMPLETE]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
return go.Figure(data=[], layout=layout)
if target is None:
if study.direction == StudyDirection.MINIMIZE:
best_values = np.minimum.accumulate([t.value for t in trials])
else:
best_values = np.maximum.accumulate([t.value for t in trials])
traces = [
go.Scatter(
x=[t.number for t in trials],
y=[t.value for t in trials],
mode="markers",
name=target_name,
),
go.Scatter(x=[t.number for t in trials], y=best_values, name="Best Value"),
]
else:
traces = [
go.Scatter(
x=[t.number for t in trials],
y=[target(t) for t in trials],
mode="markers",
name=target_name,
),
]
figure = go.Figure(data=traces, layout=layout)
return figure